Electronic storage mechanisms have enabled accumulation of massive amounts of data. For instance, data that previously required volumes of books for recordation can now be stored electronically without expense of printing paper and with a fraction of space needed for storage of paper. In one particular example, deeds and mortgages that were previously recorded in paper volumes can now be stored electronically. Moreover, advances in sensors technology now enables massive amounts of data to be collected in real-time. For instance, satellite based navigation systems, such as GPS, can determine the location of an individual or entity using satellites and receivers. The emergence of the internet and mobile computing devices has created new opportunities for data gathering in real-time. Computers and electronic storage devices can retain and store vast amounts of data from sensors and other data collection devices. Collected data relating to particular contexts and/or applications can be employed in connection with data trending and analysis, and predictions can be made as a function of received and analyzed data.
Predictive models utilized on computer systems can often produce more accurate predictive results than a human, as computer systems may have access to a substantial amount of data. For instance, a computer application can have access to data that represents traffic patterns over twenty years, whereas an individual may have experienced traffic patterns for a shorter period of time. These predictive models can be quite effective when generating predictions associated with common occurrences. Predictive models, however, can overwhelm an individual with predictions that may include superfluous information. Furthermore, predictive models can fail when used to predict events that are atypical, such as criminal activities or financial market activities. Reasons for failure can include lack of a necessary understanding of a situation, lack of critical data, infrequency of occurrence of an event, and other factors.
Simple causal-sequenced events (chain events) can be adequately modeled using existing physical models. However, activities by criminal and terrorist organizations not only attempt to hide their activities, but will act on opportunity rather than adhering to a predefined process. The impact of their opportunity based methods changes the sequence which renders the physical models ineffective for predicting future activity and events. Another example is where an adversary changes their methods, tactics, and procedures which renders the physical models ineffective for predicting future activity and events.
What is needed is a system and method that adequately models obfuscated relationships that are hidden within large complex datasets to predict future activities.